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The Latest News, Articles, and Events in Signal Processing

IEEE Transactions on Signal and Information Processing over Networks

Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems.

IEEE Transactions on Signal and Information Processing over Networks

This article presents limited feedback-based precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the min-max mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization.

IEEE Transactions on Signal and Information Processing over Networks

This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.

IEEE Transactions on Multimedia

In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. 

IEEE Transactions on Multimedia

In High Efficiency Video Coding (HEVC), multiple-QP (quantization parameter) optimization can adapt to a local video content. However, the multiple-QP implementation in the HEVC reference software (HM 16.6) achieves the best QP value for each coding block with a large amount of computational complexity.

IEEE Transactions on Multimedia

Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a single cluster without using prior information and training classifiers. In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration.

IEEE Transactions on Multimedia

Conventional video saliency detection methods frequently follow the common bottom-up thread to estimate video saliency within the short-term fashion. As a result, such methods can not avoid the obstinate accumulation of errors when the collected low-level clues are constantly ill-detected. Also, being noticed that a portion of video frames, which are not nearby the current video frame over the time axis, may potentially benefit the saliency detection in the current video frame.

IEEE Transactions on Multimedia

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. 

IEEE Transactions on Image Processing

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. 

IEEE Transactions on Image Processing

We introduce an effective fusion-based technique to enhance both day-time and night-time hazy scenes. When inverting the Koschmieder light transmission model, and by contrast with the common implementation of the popular dark-channel [1] , we estimate the airlight on image patches and not on the entire image.

IEEE Transactions on Image Processing

Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear. This paper proposes an unmixing model, named illumination invariant spectral unmixing (IISU).

This year the Signal Processing Society identified 8 Chapters who exhibited excellence in many facets of chapter management, member recruitment, and activities.

The IEEE Signal Processing Society (SPS) would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic.

There will be two exciting IEEE SPS Distinguished Lectures coming this summer. They are virtual and are free to join.

The signal processing cup, called the SP Cup, is an annual competition in the IEEE ICASSP conference. The goal of this competition is to encourage teams of 3-10 undergraduate students to solve real world problems using tools from signal processing.

The IEEE Signal Processing Society (SPS) is honored to announce the elevation of 79 of its members to the grade of IEEE Senior Member. These members have demonstrated outstanding professional performance, exhibited professional maturity through long-term experience, and established themselves as leaders in their respective IEEE-designated fields of interest.

IEEE Transactions on Information Forensics and Security

Modern System-on-Chip (SoC) designs integrate a number of third party IPs (3PIPs) that coordinate and communicate through a Network-on-Chip (NoC) fabric to realize system functionality. An important class of SoC security attack involves a rogue IP tampering with the inter-IP communication.

IEEE Transactions on Information Forensics and Security

Android inter-app communication (IAC) allows apps to request functionalities from other apps, which has been extensively used to provide a better user experience. However, IAC has also become an enticing target by attackers to launch malicious activities.

IEEE Transactions on Audio, Speech and Language Processing

Active noise control (ANC) is a technology which lowers the noise level by using the principle of destructive interference of sound wave. Even though recent developments in digital signal processing (DSP) made it possible to implement ANC algorithms in real-time, insufficient computational power is still one of the challenges to solve. In the previous research, as a way of overcoming the lack of computational power, CPU-GPU architecture was proposed so that ANC algorithms utilize the massive computing power of GPU without suffering from the block data transfer between CPU and GPU memories.

IEEE Transactions on Audio, Speech and Language Processing

This article investigates deep learning based single- and multi-channel speech dereverberation. For single-channel processing, we extend magnitude-domain masking and mapping based dereverberation to complex-domain mapping, where deep neural networks (DNNs) are trained to predict the real and imaginary (RI) components of the direct-path signal from reverberant (and noisy) ones.

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